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import torch
from torch.fx import GraphModule
from torch.fx import Node
from .pt2e.prepare import prepare
from .pt2e.qat_utils import (
_fuse_conv_bn_qat,
_fold_conv_bn_qat,
)
from .pt2e.utils import (
_get_node_name_to_scope,
_fuse_conv_bn_,
_disallow_eval_train,
)
from .pt2e.representation import reference_representation_rewrite
from .quantize_fx import _convert_to_reference_decomposed_fx
from torch.ao.quantization.quantizer import ( # noqa: F401
Quantizer,
QuantizationSpecBase,
QuantizationSpec,
FixedQParamsQuantizationSpec,
SharedQuantizationSpec,
DerivedQuantizationSpec,
QuantizationAnnotation,
)
from torch.fx.passes.infra.pass_manager import PassManager
from torch.ao.quantization.pt2e.duplicate_dq_pass import DuplicateDQPass
from torch.ao.quantization.pt2e.port_metadata_pass import PortNodeMetaForQDQ
from torch._inductor.constant_folding import constant_fold
__all__ = [
"prepare_pt2e",
"prepare_qat_pt2e",
"convert_pt2e",
]
def prepare_pt2e(
model: GraphModule,
quantizer: Quantizer,
) -> GraphModule:
"""Prepare a model for post training quantization
Args:
* `model` (torch.fx.GraphModule): a model captured by `torch.export` API
in the short term we are using `torch._export.capture_pre_autograd_graph`,
in the long term we'll migrate to some `torch.export` API
* `quantizer`: A backend specific quantizer that conveys how user want the
model to be quantized. Tutorial for how to write a quantizer can be found here:
https://pytorch.org/tutorials/prototype/pt2e_quantizer.html
Return:
A GraphModule with observer (based on quantizer annotation), ready for calibration
Example::
import torch
from torch.ao.quantization.quantize_pt2e import prepare_pt2e
from torch._export import capture_pre_autograd_graph
from torch.ao.quantization.quantizer import (
XNNPACKQuantizer,
get_symmetric_quantization_config,
)
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(5, 10)
def forward(self, x):
return self.linear(x)
# initialize a floating point model
float_model = M().eval()
# define calibration function
def calibrate(model, data_loader):
model.eval()
with torch.no_grad():
for image, target in data_loader:
model(image)
# Step 1. program capture
# NOTE: this API will be updated to torch.export API in the future, but the captured
# result shoud mostly stay the same
m = capture_pre_autograd_graph(m, *example_inputs)
# we get a model with aten ops
# Step 2. quantization
# backend developer will write their own Quantizer and expose methods to allow
# users to express how they
# want the model to be quantized
quantizer = XNNPACKQuantizer().set_global(get_symmetric_quantization_config())
m = prepare_pt2e(m, quantizer)
# run calibration
# calibrate(m, sample_inference_data)
"""
torch._C._log_api_usage_once("quantization_api.quantize_pt2e.prepare_pt2e")
original_graph_meta = model.meta
node_name_to_scope = _get_node_name_to_scope(model)
# TODO: check qconfig_mapping to make sure conv and bn are both configured
# to be quantized before fusion
# TODO: (maybe) rewrite this with subgraph_rewriter
_fuse_conv_bn_(model)
quantizer.transform_for_annotation(model)
quantizer.annotate(model)
quantizer.validate(model)
model = prepare(model, node_name_to_scope, is_qat=False)
model.meta.update(original_graph_meta)
model = _disallow_eval_train(model)
return model
def prepare_qat_pt2e(
model: GraphModule,
quantizer: Quantizer,
) -> GraphModule:
"""Prepare a model for quantization aware training
Args:
* `model` (torch.fx.GraphModule): see :func:`~torch.ao.quantization.quantize_pt2e.prepare_pt2e`
* `quantizer`: see :func:`~torch.ao.quantization.quantize_pt2e.prepare_pt2e`
Return:
A GraphModule with fake quant modules (based on quantizer annotation), ready for
quantization aware training
Example::
import torch
from torch.ao.quantization.quantize_pt2e import prepare_qat_pt2e
from torch._export import capture_pre_autograd_graph
from torch.ao.quantization.quantizer import (
XNNPACKQuantizer,
get_symmetric_quantization_config,
)
class M(torch.nn.Module):
def __init__(self):
super().__init__()
self.linear = torch.nn.Linear(5, 10)
def forward(self, x):
return self.linear(x)
# initialize a floating point model
float_model = M().eval()
# define the training loop for quantization aware training
def train_loop(model, train_data):
model.train()
for image, target in data_loader:
...
# Step 1. program capture
# NOTE: this API will be updated to torch.export API in the future, but the captured
# result shoud mostly stay the same
m = capture_pre_autograd_graph(m, *example_inputs)
# we get a model with aten ops
# Step 2. quantization
# backend developer will write their own Quantizer and expose methods to allow
# users to express how they
# want the model to be quantized
quantizer = XNNPACKQuantizer().set_global(get_symmetric_quantization_config())
m = prepare_qat_pt2e(m, quantizer)
# run quantization aware training
train_loop(prepared_model, train_loop)
"""
torch._C._log_api_usage_once("quantization_api.quantize_pt2e.prepare_qat_pt2e")
original_graph_meta = model.meta
node_name_to_scope = _get_node_name_to_scope(model)
quantizer.transform_for_annotation(model)
quantizer.annotate(model)
quantizer.validate(model)
# Perform fusion after annotate to avoid quantizing ops in the new
# subgraph that don't need to be quantized
# TODO: only fuse if conv and bn are both configured to be quantized
_fuse_conv_bn_qat(model)
model = prepare(model, node_name_to_scope, is_qat=True)
model.meta.update(original_graph_meta)
model = _disallow_eval_train(model)
return model
_QUANT_OPS = [
torch.ops.quantized_decomposed.quantize_per_tensor.default,
torch.ops.quantized_decomposed.quantize_per_tensor.tensor,
torch.ops.quantized_decomposed.quantize_per_channel.default,
]
def _quant_node_constraint(n: Node) -> bool:
"""If there is any pure ops between get_attr and quantize op they will be const propagated
e.g. get_attr(weight) -> transpose -> quantize -> dequantize*
(Note: dequantize op is not going to be constant propagated)
This filter is added because we don't want to constant fold the things that are not
related to quantization
"""
return n.op == "call_function" and n.target in _QUANT_OPS
def convert_pt2e(
model: GraphModule,
use_reference_representation: bool = False,
fold_quantize: bool = True,
) -> GraphModule:
"""Convert a calibrated/trained model to a quantized model
Args:
* `model` (torch.fx.GraphModule): calibrated/trained model
* `use_reference_representation` (bool): boolean flag to indicate whether to produce referece representation or not
* `fold_quantize` (bool): boolean flag for whether fold the quantize op or not
Returns:
quantized model, either in q/dq representation or reference representation
Example::
# prepared_model: the model produced by `prepare_pt2e`/`prepare_qat_pt2e` and calibration/training
# `convert_pt2e` produces a quantized model that represents quantized computation with
# quantize dequantize ops and fp32 ops by default.
# Please refer to
# https://pytorch.org/tutorials/prototype/pt2e_quant_ptq_static.html#convert-the-calibrated-model-to-a-quantized-model
# for detailed explanation of output quantized model
quantized_model = convert_pt2e(prepared_model)
""" # flake8: noqa
torch._C._log_api_usage_once("quantization_api.quantize_pt2e.convert_pt2e")
if not isinstance(use_reference_representation, bool):
raise ValueError(
"Unexpected argument type for `use_reference_representation`, "
f"please make sure you intend to pass argument {use_reference_representation} to convert_pt2e")
original_graph_meta = model.meta
model = _convert_to_reference_decomposed_fx(model)
model = _fold_conv_bn_qat(model)
pm = PassManager([DuplicateDQPass()])
model = pm(model).graph_module
pm = PassManager([PortNodeMetaForQDQ()])
model = pm(model).graph_module
if fold_quantize:
constant_fold(model, _quant_node_constraint)
if use_reference_representation:
model = reference_representation_rewrite(model)
model.meta.update(original_graph_meta)
model = _disallow_eval_train(model)
return model
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